autonomous spectrum awareness for smart spectrum access

Autonomous Spectrum Awarenessfor Smart Spectrum Access and Sharing

Miguel López-BenítezDepartment of Electrical Engineering and Electronics

University of Liverpool, United Kingdom

[email protected]

6G: Software Defined Radio and RF Sampling, 16 September 2021

mailto:[email protected]


Current spectrum landscape

• Spectrum use is worth well over £50bn a year to the UK economy.

• The most important resource of wireless communication systems.

• Desirable spectrum (sub-6 GHz) is crowded with legacy systems.

• Deployment of new systems:– Higher frequency bands

– Sharing of lower spectrum

2

100 GHz1 GHz

THz bandscm/mm-Wave bandsLower bands

Good propagationWide area coverage

Bandwidth 10-100 MHzDatarate 10-100 Mbps

Limited propagationReduced area coverage

Bandwidth 100-1000 MHzDatarate 100-1000 Mbps

Poor propagationSmall area coverage

Bandwidth GHzDatarate Gbps


What spectrum band?

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Unprecedented bandwidths → High data ratesUnused spectrum (free capacity & easy exploitation)Unfavourable propagation → Very short coverageUnreliable links →Momentary unavailability (URLLC)Availability/deployment: Medium / long-term

Limited RF bandwidth (and data rates)Crowded (limited capacity & difficult exploitation)Favourable propagation → Longer coverageMore reliable links (and communications)Availability/deployment: Now! / short-term

Source: Feng Hu et al., IEEE Access, vol. 6, Feb. 2018 (adapted)

• What spectrum band?– Many 6G applications will require higher capacity → mmWave/THz bands

– Many 6G applications will still require wide coverage → sub-6 GHz

• Sub-6 GHz spectrum remains critically important– Spectrum sharing is the way forward to further exploit sub-6 GHz spectrum

• Unique opportunity to also embed spectrum sharing in higher frequency bands

• Long history of spectrum sharing – Regulators’ fears overcome (WRAN, LSA/ASA, CBRS, 5G NR-U)

6GTHz bands (100-1000GHz)


Spectrum awareness approaches

4

• Databases seem to have been a preferred option (WRAN, LSA, CBRS)

• Local sensing seen as a secondary/complementary requirement

• What about local sensing only? → Autonomous spectrum awareness


Autonomous spectrum awareness

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Legacy network Mobile network monitoring spectrum occupancy pattern

Spectrum sensing

Spectrum database(local)

Spectrumoccupancy

data

Spectrumoccupancy

pattern

Real-timedata analysis

Advanced (statistical)data analysis

Short-term decisions

Long-term decisions


Autonomous spectrum awareness

6

Two-layer smart spectrum access(based on spectrum sensing)

K. Umebayashi, Y. Tamaki, M. López-Benítez, J. J. Lehtomäki, "Design of spectrum usagedetection in wideband spectrum measurements", IEEE Access, August 2019

RF/IF

ADCFFT Sensing

Signal

detection

Statistics &

activity model

Autonomous Spectrum Awareness System


Estimation of statistics based on sensing

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• How do we estimate spectrum activity statistics from sensing?

1) Sense the channel with a given sensing period (Ts)

2) Decide channel states → Idle (H0) or Busy (H1)

3) Estimate individual period durations (Ti)

4) Compute activity statistics from sequence of estimated periods:Min/max period, mean & variance (moments), distribution

H0H

1H1H

0H

0H

0· · · · H1H

0

T0

Ts T0 T1

T1

H0H

0H

0H0H

0H

1H

1H1

T1


Sources of inaccurate awareness info

8

• Finite sensing period:

– Fundamental limit to the time resolution to which idle/busy periods can be observed

– Estimated periods are integer multiples of the sensing period

– True periods have in general a continuous domain

• Limited number of observations:

– Activity statistics need to be computed based on a reduce set of period durations

H0H1 H1H0 H0H0· · · · H1H0

T0

Ts T0 T1

T1

H0H0H0 H0H0H1H1H1

T1

1 2 3 4 N· · ·

M. López-Benítez, A. Al-Tahmeesschi, D. K. Patel, J. Lehtomäki, K. Umebayashi, "Estimation of primary channel activity statistics in cognitive radio based on periodic spectrum sensing observations," IEEE Trans Wireless Comms, February 2019

A. Al-Tahmeesschi, M. López-Benítez, D. K. Patel, J. Lehtomäki, K. Umebayashi, "On the sample size for the estimation of primary activity statistics based on spectrum sensing," IEEE Trans Cognitive Comms and Networking, March 2019


Sources of inaccurate awareness info

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• Imperfect sensing performance:

H0H1 H1H0 H0H0· · · · H1H0

T0

Ts T0 T1

T1

H0H0H0 H0H0H1H1H1

T1

H0H1 H1H0 H1H0· · · · H1H0

T0

Ts T0 T1

T1

H0H0H0 H0H0H1H1H1

T1 T1 T0

Perfect Spectrum

Sensing (PSS)

(e.g., high SNR)

Imperfect Spectrum

Sensing (ISS)

(e.g., low SNR)

O. H. Toma, M. López-Benítez, D. K. Patel, K. Umebayashi, "Estimation of primary channel activity statistics in cognitive radio based on imperfect spectrum sensing," IEEE Trans Comms, April 2020


Methods for statistics estimation

10

Approaches for autonomous spectrum awareness


Reconstruction algorithms

• Approach 1: Reconstruction algorithms

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TA TB < μ0 TC < μ1 TD TE

TA TB TE+ TC + TD

TA TE+ TB + TC TD

1 2

1

2

is a false negative

is a false positive

O. H. Toma, M. López-Benítez, D. K. Patel, and K. Umebayashi, "Reconstruction algorithm for primary channel statistics estimation under imperfect spectrum sensing", IEEE WCNC 2020


Mathematical analysis

• Approach 2: Mathematical analysis

12

M. López-Benítez, A. Al-Tahmeesschi, D. K. Patel, J. Lehtomäki, K. Umebayashi, "Estimation of primary channel activity statistics in cognitive radio based on periodic spectrum sensing observations," IEEE Trans Wireless Comms, February 2019

A. Al-Tahmeesschi, M. López-Benítez, D. K. Patel, J. Lehtomäki, K. Umebayashi, "On the sample size for the estimation of primary activity statistics based on spectrum sensing," IEEE Trans Cognitive Comms and Networking, March 2019

O. H. Toma, M. López-Benítez, D. K. Patel, K. Umebayashi, "Estimation of primary channel activity statistics in cognitive radio based on imperfect spectrum sensing," IEEE Trans Comms, April 2020

𝑋 Wireless channel Spectrum detection 𝑋

𝑋 = 𝑓 𝑋, 𝑇𝑠, 𝑃𝑓𝑎, 𝑃𝑚𝑑 , 𝑁

෨𝑋 = 𝑓−1 𝑋, 𝑇𝑠, 𝑃𝑓𝑎, 𝑃𝑚𝑑 , 𝑁 ≈ 𝑋


Deep learning

• Approach 3: Deep learning

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O. H. Toma, M. López-Benítez, "Traffic Learning: a Deep Learning Approach for Obtaining Accurate Statistical Information of the Channel Traffic in Spectrum Sharing Systems", IEEE Access, September 2021A. Al-Tahmeesschi, K. Umebayashi, H. Iwata, J. Lehtomäki, M. López-Benítez, "Feature-Based Deep Neural Networks for Short-Term Prediction of WiFi Channel Occupancy Rate," IEEE Access, June 2021


Performance comparison

14


Performance comparison

15


Experimental validation

16

O. H. Toma, M. López-Benítez, "USRP-Based Prototype for Real-Time Estimationof Channel Activity Statistics in Spectrum Sharing", IEEE ISWCS 2021

Available: https://github.com/ogeen-toma/USRP-prototype-for-channel-statistics-estimation

https://github.com/ogeen-toma/USRP-prototype-for-channel-statistics-estimation


Experimental validation

17


Use case – Practical application

• Mobile networks in WiFi unlicensed spectrum (LAA)

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M. Alhulayil, M. López-Benítez, "Novel LAA waiting and transmission time configuration methods for improved LTE-LAA/Wi-Fi coexistence over unlicensed bands", IEEE Access, September 2020

➢ 3GPP Cat 4 LBT does not meet “fairness” requirements➢ Fairness can be achieved by adjusting waiting times

based on WiFi activity statistics (FWT method)➢ Aggregated capacity can be maximised by adjusting transmission times

based on WiFi activity statistics (DynTxOP method)

6G: Software Defined Radio and RF Sampling, 16 September 2021 19

for your attention !


Manuscript submission: November 1, 2021First review completed: December 15, 2021Revised manuscript due: January 15, 2022Final editorial decisions: February 1, 2022Final manuscript due: February 15, 2022Final publication: Quarter 1, 2022

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autonomous spectrum awareness for smart spectrum access

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